With increasing Internet data transfer speeds and the prosperity of WEB 2.0, the amount of image data on the Internet is ever-growing. Image-based websites, such as FLICKR®, PICASA®, YOU-TUBE®, etc., are growing in popularity, making online content-based image management more important than ever. Since new image data is being uploaded to the Internet all the time, how to efficiently organize, index, and retrieve desired image data is a constant challenge. One particular challenge is finding images with a similar characteristic, such as including similar visual subject matter. Word-based searches for clothing are not effective if the clothing is not described in metadata associated with the image.
Searching for similar clothes in photo collections is non-trivial as the photos are usually taken under completely uncontrolled environment. The clothes portion of visual images can have large variations such as geometric deformation, occlusion, size, background, and photometric variability from illumination and pose, which cause significant interference on clothes retrieval. These variations can impact similarly categorizing image data of subjects wearing similar clothing, and hinder retrieval based on the similar clothing. Similar clothing can help identify images of a particular person who wears the clothing such as in family photo collections, in security applications such as tracking an individual across a number of camera installations, in electronically shopping for the clothing, for searching to find a celebrity wearing certain clothing, and numerous other applications. As such, an improved methodology of clothing search in images can be beneficial.